The ability to accurately forecast inventory is important for many product selling businesses. An accurate forecast allows a product seller to know what it can commit to sell to a buyer, which affects the negotiation of many supply contracts. Originally, forecasting inventory required a seller to determine an amount of physical goods it had at its disposal (e.g., in a warehouse, etc.). This task has become much more complex in the internet-age, in which products available for sale are not always physical goods. For example, online content publishers often sell space on their webpages as products. As such, rather than determining an amount of physical goods sitting in a warehouse, the inventory forecast involves projecting the number of viewers (in some cases, meeting certain targeting attributes) that will view the website. The forecast is further complicated by the fact that the criteria for multiple orders for webpage space (“order lines”) can be satisfied by a single webpage viewer; and, given that each webpage has a finite amount of space to sell, the sale of space to one order line necessarily takes inventory away from the other order lines (a concept sometimes referred to as “cannibalization”).
A common metric in forecasting webpage space is an impression. In general, an impression is the presentation of a particular creative to a viewer. Traditionally, forecasting webpage space inventory (sometimes referred to herein as “capacity”) has focused on a pure static impressions-based model (sometimes referred to herein as the “traditional model”), in which solely a determination of past impressions to particular viewers is used to forecast the number of future impressions.
U.S. Pat. Nos. 9,092,807; 8,392,248; 8,412,572; and 9,082,138, which largely share a common specification, and all of which are incorporated by reference herein in their entireties, provide a detailed description of a pure impressions-based forecast model. The model works well when the impressions are static, because the number of past impressions provides a reliable indication of past inventory.
In more recent times, video-based content has become much more popular and is being distributed much more frequently over the internet. As such, in addition to (or as an alternative from) static content (e.g., sidebar windows, banner windows, etc.), publishers are now selling portions of the video space to buyers. However, for reasons discussed below, the forecasting models have some limitations when forecasting video content. As such, there is a need for an improved forecasting technique better suited to forecasting non-static (e.g., video-based) inventory.